Technical Field
The present disclosure pertains to image processing and information retrieval and, more particularly, to an image-centric process of capturing, clustering, and analyzing image data to determine and measure brand interactions in social media and other online platforms.
Description of the Related Art
Social media is widely accepted today as an important marketing, outreach, and advertising channel for product and service brands. As brand owners invest resources in building their social media presence, it is critical that they are able to accurately measure the impact and the nature of brand presence.
As a starting point, a brand owner may need to understand the overall size of their brand presence (as measured, for example, in a count of brand interactions, brand impressions, or brand followers). Diving deeper, a brand owner may also need to understand which social media interactions are having the highest ROI; for example, which posts are receiving the most attention (as measured in likes, comments, repins, impressions, etc.).
Traditionally, this type of social media analysis has been conducted using keywords or hashtags. This type of text-based social media analysis is well understand and has been extensively publicly described. Current logo and object recognition techniques identify a particular pattern (i.e., a logo or object) that may only be part of an image. In addition, logo and object recognition techniques aim to be invariant to in- or out-of-plane rotations.
As the prevalence of image sharing on social media channels has increased, the utility of purely text-based social media analysis has decreased. When consumers share images, they will often fail to provide adequate captions or other metadata; they are assuming, rightly, that an image speaks for itself. While an image may be worth a thousand words, it is equivalent to zero words from the perspective of a purely-textual approach to social media analysis. To make sense of visual social media posts, an image-centric approach is needed.